This project explores the various convolutional neural network (CNN) frameworks for processeing images through convolutional, activation, pooling, and fully connected layers, capturing hierarchical features and learning to map input images to their respective classes during training using various helpful packages in Python. Various CNN architectures applied in the analysis to learn features and patterns at different levels of abstraction in images included CNN Without Regularization, CNN With Dropout Regularization, CNN With Batch Normalization Regularization and CNN With Dropout and Batch Normalization Regularization. The different CNN algorithms were evaluated using the categorical cross entropy loss which measures the difference between the predicted probability distribution and the true distribution of the class labels. Model multi-classification performance was measured using Accuracy, Precision, Recall and F1 Score. All results were consolidated in a Summary presented at the end of the document.
A convolutional neural network model is a type of neural network architecture specifically designed for image classification and computer vision tasks by automatically learning hierarchical features directly from raw pixel data. The core building block of a CNN is the convolutional layer. Convolution operations apply learnable filters (kernels) to input images to detect patterns such as edges, textures, and more complex structures. The layers systematically learn hierarchical features from low-level (e.g., edges) to high-level (e.g., object parts) as the network deepens. Filters are shared across the entire input space, enabling the model to recognize patterns regardless of their spatial location. After convolutional operations, an activation function is applied element-wise to introduce non-linearity and allow the model to learn complex relationships between features. Pooling layers downsample the spatial dimensions of the feature maps, reducing the computational load and the number of parameters in the network - creating spatial hierarchy and translation invariance. Fully connected layers process the flattened features to make predictions and produce an output vector that corresponds to class probabilities using an activation function. The CNN is trained using backpropagation and optimization algorithms. A loss function is used to measure the difference between predicted and actual labels. The network adjusts its weights to minimize this loss. Gradients are calculated with respect to the loss, and the weights are updated accordingly through a backpropagation mechanism.
A subset of an open COVID-19 Radiography Dataset from Kaggle (with all credits attributed to Preet Viradiya, Juliana Negrini De Araujo, Tawsifur Rahman, Muhammad Chowdhury and Amith Khandakar) was used for the analysis as consolidated from the following primary sources:
This study hypothesized that images contain a hierarchy of features which allows the differentiation and classification across various image categories.
The target variable for the study is:
The hierarchical representation of image features enables the network to transform raw pixel data into a meaningful and compact representation, allowing it to make accurate predictions during image classification. The different features automatically learned during the training process are as follows:
##################################
# Installing important packages
##################################
# !pip install mlxtend
# !pip install --upgrade tensorflow
# !pip install opencv-python
# !pip install keras==2.12.0
##################################
# Loading Python Libraries
# for Data Loading,
# Data Preprocessing and
# Exploratory Data Analysis
##################################
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
%matplotlib inline
import tensorflow as tf
import keras
from PIL import Image
from glob import glob
import cv2
import os
import random
WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\losses.py:2664: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
##################################
# Loading Python Libraries
# for Model Development
##################################
from keras import backend as K
from keras import regularizers
from keras.models import Sequential, Model,load_model
from keras.layers import Activation, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, MaxPool2D, AveragePooling2D, GlobalMaxPooling2D, BatchNormalization
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils.np_utils import to_categorical
from keras.optimizers import Adam, SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from tensorflow.keras.utils import img_to_array, array_to_img
##################################
# Loading Python Libraries
# for Model Evaluation
##################################
from keras.metrics import PrecisionAtRecall, Recall
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_fscore_support, accuracy_score
##################################
# Setting random seed options
# for the analysis
##################################
def set_seed(seed=88888888):
np.random.seed(seed)
tf.random.set_seed(seed)
keras.utils.set_random_seed(seed)
random.seed(seed)
tf.config.experimental.enable_op_determinism()
os.environ['TF_DETERMINISTIC_OPS'] = "1"
os.environ['TF_CUDNN_DETERMINISM'] = "1"
os.environ['PYTHONHASHSEED'] = str(seed)
set_seed()
##################################
# Loading the dataset
##################################
path = 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset/'
##################################
# Defining the image category levels
##################################
diagnosis_code_dictionary = {'COVID': 0,
'Normal': 1,
'Viral Pneumonia': 2}
##################################
# Defining the image category descriptions
##################################
diagnosis_description_dictionary = {'COVID': 'Covid-19',
'Normal': 'Healthy',
'Viral Pneumonia': 'Viral Pneumonia'}
##################################
# Consolidating the image path
##################################
imageid_path_dictionary = {os.path.splitext(os.path.basename(x))[0]: x for x in glob(os.path.join(path, '*','*.png'))}
##################################
# Taking a snapshot of the dictionary
##################################
dict(list(imageid_path_dictionary.items())[0:5])
{'COVID-1': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-1.png',
'COVID-10': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-10.png',
'COVID-100': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-100.png',
'COVID-1000': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-1000.png',
'COVID-1001': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-1001.png'}
##################################
# Consolidating the information
# from the dataset
# into a dataframe
##################################
xray_images = pd.DataFrame.from_dict(imageid_path_dictionary, orient = 'index').reset_index()
xray_images.columns = ['Image_ID','Path']
classes = xray_images.Image_ID.str.split('-').str[0]
xray_images['Diagnosis'] = classes
xray_images['Target'] = xray_images['Diagnosis'].map(diagnosis_code_dictionary.get)
xray_images['Class'] = xray_images['Diagnosis'].map(diagnosis_description_dictionary.get)
##################################
# Performing a general exploration of the dataset
##################################
print('Dataset Dimensions: ')
display(xray_images.shape)
Dataset Dimensions:
(3600, 5)
##################################
# Listing the column names and data types
##################################
print('Column Names and Data Types:')
display(xray_images.dtypes)
Column Names and Data Types:
Image_ID object Path object Diagnosis object Target int64 Class object dtype: object
##################################
# Taking a snapshot of the dataset
##################################
xray_images.head()
| Image_ID | Path | Diagnosis | Target | Class | |
|---|---|---|---|---|---|
| 0 | COVID-1 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 |
| 1 | COVID-10 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 |
| 2 | COVID-100 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 |
| 3 | COVID-1000 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 |
| 4 | COVID-1001 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 |
##################################
# Performing a general exploration of the numeric variables
##################################
print('Numeric Variable Summary:')
display(xray_images.describe(include='number').transpose())
Numeric Variable Summary:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Target | 3600.0 | 1.0 | 0.81661 | 0.0 | 0.0 | 1.0 | 2.0 | 2.0 |
##################################
# Performing a general exploration of the object variable
##################################
print('Object Variable Summary:')
display(xray_images.describe(include='object').transpose())
Object Variable Summary:
| count | unique | top | freq | |
|---|---|---|---|---|
| Image_ID | 3600 | 3600 | COVID-1 | 1 |
| Path | 3600 | 3600 | C:/Users/John pauline magno/Python Notebooks/C... | 1 |
| Diagnosis | 3600 | 3 | COVID | 1200 |
| Class | 3600 | 3 | Covid-19 | 1200 |
##################################
# Performing a general exploration of the target variable
##################################
xray_images.Diagnosis.value_counts()
COVID 1200 Normal 1200 Viral Pneumonia 1200 Name: Diagnosis, dtype: int64
##################################
# Performing a general exploration of the target variable
##################################
xray_images.Diagnosis.value_counts(normalize=True)
COVID 0.333333 Normal 0.333333 Viral Pneumonia 0.333333 Name: Diagnosis, dtype: float64
Data quality findings based on assessment are as follows:
##################################
# Counting the number of duplicated images
##################################
xray_images.duplicated().sum()
0
##################################
# Gathering the number of null images
##################################
xray_images.isnull().sum()
Image_ID 0 Path 0 Diagnosis 0 Target 0 Class 0 dtype: int64
##################################
# Including the pixel information
# of the actual images
# in array format
# into a dataframe
##################################
xray_images['Image'] = xray_images['Path'].map(lambda x: np.asarray(Image.open(x).resize((75,75))))
##################################
# Listing the column names and data types
##################################
print('Column Names and Data Types:')
display(xray_images.dtypes)
Column Names and Data Types:
Image_ID object Path object Diagnosis object Target int64 Class object Image object dtype: object
##################################
# Taking a snapshot of the dataset
##################################
xray_images.head()
| Image_ID | Path | Diagnosis | Target | Class | Image | |
|---|---|---|---|---|---|---|
| 0 | COVID-1 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 | [[15, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... |
| 1 | COVID-10 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 | [[129, 125, 123, 121, 119, 117, 114, 104, 104,... |
| 2 | COVID-100 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 | [[11, 0, 0, 3, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0... |
| 3 | COVID-1000 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 | [[42, 39, 38, 42, 38, 35, 31, 26, 24, 24, 24, ... |
| 4 | COVID-1001 | C:/Users/John pauline magno/Python Notebooks/C... | COVID | 0 | Covid-19 | [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0,... |
##################################
# Taking a snapshot of the dataset
##################################
n_samples = 5
fig, m_axs = plt.subplots(3, n_samples, figsize = (3*n_samples, 8))
for n_axs, (type_name, type_rows) in zip(m_axs, xray_images.sort_values(['Diagnosis']).groupby('Diagnosis')):
n_axs[2].set_title(type_name, fontsize = 14, weight = 'bold')
for c_ax, (_, c_row) in zip(n_axs, type_rows.sample(n_samples, random_state=1).iterrows()):
picture = c_row['Path']
image = cv2.imread(picture)
c_ax.imshow(image)
c_ax.axis('off')
##################################
# Sampling a single image
##################################
samples, features = xray_images.shape
plt.figure()
pic_id = random.randrange(0, samples)
picture = xray_images['Path'][pic_id]
image = cv2.imread(picture)
<Figure size 640x480 with 0 Axes>
##################################
# Plotting using subplots
##################################
plt.figure(figsize=(15, 5))
##################################
# Formulating the original image
##################################
plt.subplot(1, 4, 1)
plt.imshow(image)
plt.title('Original Image', fontsize = 14, weight = 'bold')
plt.axis('off')
##################################
# Formulating the blue channel
##################################
plt.subplot(1, 4, 2)
plt.imshow(image[ : , : , 0])
plt.title('Blue Channel', fontsize = 14, weight = 'bold')
plt.axis('off')
##################################
# Formulating the green channel
##################################
plt.subplot(1, 4, 3)
plt.imshow(image[ : , : , 1])
plt.title('Green Channel', fontsize = 14, weight = 'bold')
plt.axis('off')
##################################
# Formulating the red channel
##################################
plt.subplot(1, 4, 4)
plt.imshow(image[ : , : , 2])
plt.title('Red Channel', fontsize = 14, weight = 'bold')
plt.axis('off')
##################################
# Consolidating all images
##################################
plt.show()
##################################
# Determining the image shape
##################################
print('Image Shape:')
display(image.shape)
Image Shape:
(299, 299, 3)
##################################
# Determining the image height
##################################
print('Image Height:')
display(image.shape[0])
Image Height:
299
##################################
# Determining the image width
##################################
print('Image Width:')
display(image.shape[0])
Image Width:
299
##################################
# Determining the image dimension
##################################
print('Image Dimension:')
display(image.ndim)
Image Dimension:
3
##################################
# Determining the image size
##################################
print('Image Size:')
display(image.size)
Image Size:
268203
##################################
# Determining the image data type
##################################
print('Image Data Type:')
display(image.dtype)
Image Data Type:
dtype('uint8')
##################################
# Determining the maximum RGB value
##################################
print('Image Maximum RGB:')
display(image.max())
Image Maximum RGB:
205
##################################
# Determining the minimum RGB value
##################################
print('Image Minimum RGB:')
display(image.min())
Image Minimum RGB:
10
##################################
# Identifying the path for the images
# and defining image categories
##################################
path = 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset'
classes=["COVID", "Normal", "Viral Pneumonia"]
num_classes = len(classes)
batch_size = 16
##################################
# Creating subsets of images
# for model training and
# setting the parameters for
# real-time data augmentation
# at each epoch
##################################
set_seed()
train_datagen = ImageDataGenerator(rescale=1./255,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
vertical_flip=True,
shear_range=0.2,
zoom_range=0.2,
validation_split=0.2)
##################################
# Loading the model training images
##################################
train_gen = train_datagen.flow_from_directory(directory=path,
target_size=(299, 299),
class_mode='categorical',
subset='training',
shuffle=True,
classes=classes,
batch_size=batch_size,
color_mode="grayscale")
Found 2880 images belonging to 3 classes.
##################################
# Loading samples of augmented images
# for the training set
##################################
fig, axes = plt.subplots(1, 5, figsize=(15, 3))
for i in range(5):
batch = next(train_gen)
images, labels = batch
axes[i].imshow(images[0]) # Display the first image in the batch
axes[i].set_title(f"Label: {labels[0]}")
axes[i].axis('off')
plt.show()
##################################
# Creating subsets of images
# for model validation
# setting the parameters for
# real-time data augmentation
# at each epoch
##################################
set_seed()
test_datagen = ImageDataGenerator(rescale=1./255,
validation_split=0.2)
##################################
# Loading the model evaluation images
##################################
test_gen = test_datagen.flow_from_directory(directory=path,
target_size=(299, 299),
class_mode='categorical',
subset='validation',
shuffle=False,
classes=classes,
batch_size=batch_size,
color_mode="grayscale")
Found 720 images belonging to 3 classes.
##################################
# Loading samples of augmented images
# for the validation set
##################################
fig, axes = plt.subplots(1, 5, figsize=(15, 3))
for i in range(5):
batch = next(test_gen)
images, labels = batch
axes[i].imshow(images[0])
axes[i].set_title(f"Label: {labels[0]}")
axes[i].axis('off')
plt.show()
##################################
# Consolidating summary statistics
# for the image pixel values
##################################
mean_val = []
std_dev_val = []
max_val = []
min_val = []
for i in range(0, samples):
mean_val.append(xray_images['Image'][i].mean())
std_dev_val.append(np.std(xray_images['Image'][i]))
max_val.append(xray_images['Image'][i].max())
min_val.append(xray_images['Image'][i].min())
imageEDA = xray_images.loc[:,['Image', 'Class','Path']]
imageEDA['Mean'] = mean_val
imageEDA['StDev'] = std_dev_val
imageEDA['Max'] = max_val
imageEDA['Min'] = min_val
##################################
# Formulating the mean distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Mean', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Mean Distribution by Category', fontsize=14, weight='bold');
##################################
# Formulating the maximum distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Max', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Maximum Distribution by Category', fontsize=14, weight='bold');
##################################
# Formulating the minimum distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Min', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Minimum Distribution by Category', fontsize=14, weight='bold');
##################################
# Formulating the standard deviation distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'StDev', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Standard Deviation Distribution by Category', fontsize=14, weight='bold');
##################################
# Formulating the mean and standard deviation
# scatterplot distribution
# by category of the image pixel values
##################################
plt.figure(figsize=(10,6))
sns.set(style="ticks", font_scale = 1)
ax = sns.scatterplot(data=imageEDA, x="Mean", y=imageEDA['StDev'], hue='Class', alpha=0.5)
sns.despine(top=True, right=True, left=False, bottom=False)
plt.xticks(rotation=0, fontsize = 12)
ax.set_xlabel('Image Pixel Mean',fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Standard Deviation', fontsize=14, weight='bold')
plt.title('Image Pixel Mean and Standard Deviation Distribution', fontsize = 14, weight='bold');
##################################
# Formulating the mean and standard deviation
# scatterplot distribution
# by category of the image pixel values
##################################
scatterplot = sns.FacetGrid(imageEDA, col="Class", height=6)
scatterplot.map_dataframe(sns.scatterplot, x='Mean', y='StDev', alpha=0.5)
scatterplot.set_titles(col_template="{col_name}", row_template="{row_name}", size=18)
scatterplot.fig.subplots_adjust(top=.8)
scatterplot.fig.suptitle('Image Pixel Mean and Standard Deviation Distribution', fontsize=14, weight='bold')
axes = scatterplot.axes.flatten()
axes[0].set_ylabel('Image Pixel Standard Deviation')
for ax in axes:
ax.set_xlabel('Image Pixel Mean')
scatterplot.fig.tight_layout()
##################################
# Formulating the mean and standard deviation
# scatterplot distribution
# of the image pixel values
# represented as actual images
##################################
def getImage(path):
return OffsetImage(cv2.imread(path),zoom = 0.1)
DF_sample = imageEDA.sample(frac=1.0, replace=False, random_state=1)
paths = DF_sample['Path']
fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(40,220)
ax.set_ylim(10,110)
plt.title('Overall: Image Pixel Mean and Standard Deviation Distribution', fontsize=14, weight='bold');
for x0, y0, path in zip(DF_sample['Mean'], DF_sample['StDev'],paths):
ab = AnnotationBbox(getImage(path), (x0, y0), frameon=False)
ax.add_artist(ab)
##################################
# Formulating the mean and standard deviation
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Covid-19 class
##################################
path_covid = 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset/COVID/'
imageEDA_covid = imageEDA.loc[imageEDA['Class'] == 'Covid-19']
def getImage(path_covid):
return OffsetImage(cv2.imread(path_covid),zoom = 0.1)
DF_sample = imageEDA_covid.sample(frac=1.0, replace=False, random_state=1)
paths = DF_sample['Path']
fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(40,220)
ax.set_ylim(10,110)
plt.title('Covid-19: Image Pixel Mean and Standard Deviation Distribution', fontsize=14, weight='bold');
for x0, y0, path_covid in zip(DF_sample['Mean'], DF_sample['StDev'],paths):
ab = AnnotationBbox(getImage(path_covid), (x0, y0), frameon=False)
ax.add_artist(ab)
##################################
# Formulating the mean and standard deviation
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Viral Pneumonia class
##################################
path_viral_pneumonia = 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset/Viral Pneumonia/'
imageEDA_viral_pneumonia = imageEDA.loc[imageEDA['Class'] == 'Viral Pneumonia']
def getImage(path_viral_pneumonia):
return OffsetImage(cv2.imread(path_viral_pneumonia),zoom = 0.1)
DF_sample = imageEDA_viral_pneumonia.sample(frac=1.0, replace=False, random_state=1)
paths = DF_sample['Path']
fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(40,220)
ax.set_ylim(10,110)
plt.title('Viral Pneumonia: Image Pixel Mean and Standard Deviation Distribution', fontsize=14, weight='bold');
for x0, y0, path_viral_pneumonia in zip(DF_sample['Mean'], DF_sample['StDev'],paths):
ab = AnnotationBbox(getImage(path_viral_pneumonia), (x0, y0), frameon=False)
ax.add_artist(ab)
##################################
# Formulating the mean and standard deviation
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Viral Pneumonia class
##################################
path_normal = 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset/Normal/'
imageEDA_normal = imageEDA.loc[imageEDA['Class'] == 'Healthy']
def getImage(path_normal):
return OffsetImage(cv2.imread(path_normal),zoom = 0.1)
DF_sample = imageEDA_normal.sample(frac=1.0, replace=False, random_state=1)
paths = DF_sample['Path']
fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(40,220)
ax.set_ylim(10,110)
plt.title('Healthy: Image Pixel Mean and Standard Deviation Distribution', fontsize=14, weight='bold');
for x0, y0, path_normal in zip(DF_sample['Mean'], DF_sample['StDev'],paths):
ab = AnnotationBbox(getImage(path_normal), (x0, y0), frameon=False)
ax.add_artist(ab)
##################################
# Defining a function for
# plotting the loss profile
# of the training and validation sets
#################################
def plot_training_history(history, model_name):
plt.figure(figsize=(10,6))
plt.plot(history.history['loss'], label='Train')
plt.plot(history.history['val_loss'], label='Validation')
plt.title(f'{model_name} Training Loss', fontsize = 16, weight = 'bold', pad=20)
plt.ylim(0, 5)
plt.xlabel('Epoch', fontsize = 14, weight = 'bold',)
plt.ylabel('Loss', fontsize = 14, weight = 'bold',)
plt.legend()
plt.show()
##################################
# Formulating the network architecture
# for CNN with no regularization
##################################
set_seed()
batch_size = 16
model_nr = Sequential()
model_nr.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='Same', input_shape=(299, 299, 1)))
model_nr.add(MaxPooling2D(pool_size=(2, 2)))
model_nr.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model_nr.add(MaxPooling2D(pool_size=(2, 2)))
model_nr.add(Flatten())
model_nr.add(Dense(units=128, activation='relu'))
model_nr.add(Dense(units=num_classes, activation='softmax'))
##################################
# Compiling the network layers
##################################
model_nr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall()])
WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead. WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\layers\pooling\max_pooling2d.py:160: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead. WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\optimizers\__init__.py:300: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
##################################
# Displaying the model summary
# for CNN with no regularization
##################################
print(model_nr.summary())
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 299, 299, 32) 320
max_pooling2d (MaxPooling2D (None, 149, 149, 32) 0
)
conv2d_1 (Conv2D) (None, 149, 149, 64) 18496
max_pooling2d_1 (MaxPooling (None, 74, 74, 64) 0
2D)
flatten (Flatten) (None, 350464) 0
dense (Dense) (None, 128) 44859520
dense_1 (Dense) (None, 3) 387
=================================================================
Total params: 44,878,723
Trainable params: 44,878,723
Non-trainable params: 0
_________________________________________________________________
None
##################################
# Displaying the model layers
# for CNN with no regularization
##################################
model_nr_layer_names = [layer.name for layer in model_nr.layers]
print("Layer Names:", model_nr_layer_names)
Layer Names: ['conv2d', 'max_pooling2d', 'conv2d_1', 'max_pooling2d_1', 'flatten', 'dense', 'dense_1']
##################################
# Displaying the number of weights
# for each model layer
# for CNN with no regularization
##################################
for layer in model_nr.layers:
if hasattr(layer, 'weights'):
print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")
Layer: conv2d, Number of Weights: 2 Layer: max_pooling2d, Number of Weights: 0 Layer: conv2d_1, Number of Weights: 2 Layer: max_pooling2d_1, Number of Weights: 0 Layer: flatten, Number of Weights: 0 Layer: dense, Number of Weights: 2 Layer: dense_1, Number of Weights: 2
##################################
# Displaying the number of weights
# for each model layer
# for CNN with no regularization
##################################
total_parameters = 0
for layer in model_nr.layers:
layer_parameters = layer.count_params()
total_parameters += layer_parameters
print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)
Layer: conv2d, Parameters: 320 Layer: max_pooling2d, Parameters: 0 Layer: conv2d_1, Parameters: 18496 Layer: max_pooling2d_1, Parameters: 0 Layer: flatten, Parameters: 0 Layer: dense, Parameters: 44859520 Layer: dense_1, Parameters: 387 Total Parameters in the Model: 44878723
##################################
# Fitting the model
# for CNN with no regularization
##################################
epochs = 100
set_seed()
model_nr_history = model_nr.fit(train_gen,
steps_per_epoch=len(train_gen) // batch_size,
validation_steps=len(test_gen) // batch_size,
validation_data=test_gen,
epochs=epochs,
verbose=0)
WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\utils\tf_utils.py:490: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.
##################################
# Evaluating the model
# for CNN with no regularization
# on the independent validation set
##################################
model_nr_y_pred = model_nr.predict(test_gen)
45/45 [==============================] - 4s 80ms/step
##################################
# Plotting the loss profile
# for CNN with no regularization
# on the training and validation sets
##################################
plot_training_history(model_nr_history, 'CNN With No Regularization : ')
##################################
# Consolidating the predictions
# for CNN with no regularization
# on the validation set
##################################
model_nr_predictions = np.array(list(map(lambda x: np.argmax(x), model_nr_y_pred)))
model_nr_y_true = test_gen.classes
##################################
# Formulating the confusion matrix
# for CNN with no regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_nr_y_true, model_nr_predictions), columns=classes, index =classes)
##################################
# Plotting the confusion matrix
# for CNN with no regularization
# on the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250,cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold')
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With No Regularization : Validation Set Confusion Matrix',fontsize = 14, weight = 'bold',pad=20);
##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()
##################################
# Calculating the model accuracy
# for CNN with no regularization
# for the entire validation set
##################################
model_nr_acc = accuracy_score(model_nr_y_true, model_nr_predictions)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with no regularization
# for the entire validation set
##################################
model_nr_results_all = precision_recall_fscore_support(model_nr_y_true, model_nr_predictions, average='macro',zero_division = 1)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with no regularization
# for each category of the validation set
##################################
model_nr_results_class = precision_recall_fscore_support(model_nr_y_true, model_nr_predictions, average=None, zero_division = 1)
##################################
# Consolidating all model evaluation metrics
# for CNN with no regularization
##################################
metric_columns = ['Precision','Recall','F-Score','Support']
model_nr_all_df = pd.concat([pd.DataFrame(list(model_nr_results_class)).T,pd.DataFrame(list(model_nr_results_all)).T])
model_nr_all_df.columns = metric_columns
model_nr_all_df.index = ['COVID', 'Normal', 'Viral Pneumonia','Total']
model_nr_all_df
| Precision | Recall | F-Score | Support | |
|---|---|---|---|---|
| COVID | 0.890295 | 0.879167 | 0.884696 | 240.0 |
| Normal | 0.671378 | 0.791667 | 0.726577 | 240.0 |
| Viral Pneumonia | 0.810000 | 0.675000 | 0.736364 | 240.0 |
| Total | 0.790558 | 0.781944 | 0.782546 | NaN |
##################################
# Consolidating all model evaluation metrics
# for CNN with no regularization
##################################
model_nr_model_list = []
model_nr_measure_list = []
model_nr_category_list = []
model_nr_value_list = []
for i in range(3):
for j in range(4):
model_nr_model_list.append('CNN_NR')
model_nr_measure_list.append(metric_columns[i])
model_nr_category_list.append(model_nr_all_df.index[j])
model_nr_value_list.append(model_nr_all_df.iloc[j,i])
model_nr_all_summary = pd.DataFrame(zip(model_nr_model_list,
model_nr_measure_list,
model_nr_category_list,
model_nr_value_list),
columns=['CNN.Model.Name',
'Model.Metric',
'Image.Category',
'Metric.Value'])
##################################
# Formulating the network architecture
# for CNN with dropout regularization
##################################
set_seed()
batch_size = 16
model_dr = Sequential()
model_dr.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='Same', input_shape=(299, 299, 1)))
model_dr.add(MaxPooling2D(pool_size=(2, 2)))
model_dr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu'))
model_dr.add(Dropout(rate=0.25))
model_dr.add(MaxPooling2D(pool_size=(2, 2)))
model_dr.add(Flatten())
model_dr.add(Dense(units=128, activation='relu'))
model_dr.add(Dense(units=num_classes, activation='softmax'))
##################################
# Compiling the network layers
##################################
model_dr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall()])
##################################
# Displaying the model summary
# for CNN with dropout regularization
##################################
print(model_dr.summary())
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 299, 299, 32) 320
max_pooling2d (MaxPooling2D (None, 149, 149, 32) 0
)
conv2d_1 (Conv2D) (None, 149, 149, 64) 18496
dropout (Dropout) (None, 149, 149, 64) 0
max_pooling2d_1 (MaxPooling (None, 74, 74, 64) 0
2D)
flatten (Flatten) (None, 350464) 0
dense (Dense) (None, 128) 44859520
dense_1 (Dense) (None, 3) 387
=================================================================
Total params: 44,878,723
Trainable params: 44,878,723
Non-trainable params: 0
_________________________________________________________________
None
##################################
# Displaying the model layers
# for CNN with dropout regularization
##################################
model_dr_layer_names = [layer.name for layer in model_dr.layers]
print("Layer Names:", model_dr_layer_names)
Layer Names: ['conv2d', 'max_pooling2d', 'conv2d_1', 'dropout', 'max_pooling2d_1', 'flatten', 'dense', 'dense_1']
##################################
# Displaying the number of weights
# for each model layer
# for CNN with dropout regularization
##################################
for layer in model_dr.layers:
if hasattr(layer, 'weights'):
print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")
Layer: conv2d, Number of Weights: 2 Layer: max_pooling2d, Number of Weights: 0 Layer: conv2d_1, Number of Weights: 2 Layer: dropout, Number of Weights: 0 Layer: max_pooling2d_1, Number of Weights: 0 Layer: flatten, Number of Weights: 0 Layer: dense, Number of Weights: 2 Layer: dense_1, Number of Weights: 2
##################################
# Displaying the number of weights
# for each model layer
# for CNN with dropout regularization
##################################
total_parameters = 0
for layer in model_dr.layers:
layer_parameters = layer.count_params()
total_parameters += layer_parameters
print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)
Layer: conv2d, Parameters: 320 Layer: max_pooling2d, Parameters: 0 Layer: conv2d_1, Parameters: 18496 Layer: dropout, Parameters: 0 Layer: max_pooling2d_1, Parameters: 0 Layer: flatten, Parameters: 0 Layer: dense, Parameters: 44859520 Layer: dense_1, Parameters: 387 Total Parameters in the Model: 44878723
##################################
# Fitting the model
# for CNN with dropout regularization
##################################
epochs = 100
set_seed()
model_dr_history = model_dr.fit(train_gen,
steps_per_epoch=len(train_gen) // batch_size,
validation_steps=len(test_gen) // batch_size,
validation_data=test_gen,
epochs=epochs,
verbose=0)
##################################
# Evaluating the model
# for CNN with dropout regularization
# on the independent validation set
##################################
model_dr_y_pred = model_dr.predict(test_gen)
45/45 [==============================] - 4s 79ms/step
##################################
# Plotting the loss profile
# for CNN with dropout regularization
# on the training and validation sets
##################################
plot_training_history(model_dr_history, 'CNN With Dropout Regularization : ')
##################################
# Consolidating the predictions
# for CNN with dropout regularization
# on the validation set
##################################
model_dr_predictions = np.array(list(map(lambda x: np.argmax(x), model_dr_y_pred)))
model_dr_y_true=test_gen.classes
##################################
# Formulating the confusion matrix
# for CNN with dropout regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_dr_y_true, model_dr_predictions), columns=classes, index =classes)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250, cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold')
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Dropout Regularization : Validation Set Confusion Matrix',fontsize = 14, weight = 'bold', pad=20);
##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()
##################################
# Calculating the model accuracy
# for CNN with dropout regularization
# for the entire validation set
##################################
model_dr_acc = accuracy_score(model_dr_y_true, model_dr_predictions)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for the entire validation set
##################################
model_dr_results_all = precision_recall_fscore_support(model_dr_y_true, model_dr_predictions, average='macro',zero_division = 1)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for each category of the validation set
##################################
model_dr_results_class = precision_recall_fscore_support(model_dr_y_true, model_dr_predictions, average=None, zero_division = 1)
##################################
# Consolidating all model evaluation metrics
# for CNN with dropout regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_dr_all_df = pd.concat([pd.DataFrame(list(model_dr_results_class)).T,pd.DataFrame(list(model_dr_results_all)).T])
model_dr_all_df.columns = metric_columns
model_dr_all_df.index = ['COVID', 'Normal', 'Viral Pneumonia','Total']
model_dr_all_df
| Precision | Recall | F-Score | Support | |
|---|---|---|---|---|
| COVID | 0.906504 | 0.929167 | 0.917695 | 240.0 |
| Normal | 0.904255 | 0.708333 | 0.794393 | 240.0 |
| Viral Pneumonia | 0.758741 | 0.904167 | 0.825095 | 240.0 |
| Total | 0.856500 | 0.847222 | 0.845728 | NaN |
##################################
# Consolidating all model evaluation metrics
# for CNN with dropout regularization
##################################
model_dr_model_list = []
model_dr_measure_list = []
model_dr_category_list = []
model_dr_value_list = []
for i in range(3):
for j in range(4):
model_dr_model_list.append('CNN_DR')
model_dr_measure_list.append(metric_columns[i])
model_dr_category_list.append(model_dr_all_df.index[j])
model_dr_value_list.append(model_dr_all_df.iloc[j,i])
model_dr_all_summary = pd.DataFrame(zip(model_dr_model_list,
model_dr_measure_list,
model_dr_category_list,
model_dr_value_list),
columns=['CNN.Model.Name',
'Model.Metric',
'Image.Category',
'Metric.Value'])
##################################
# Formulating the network architecture
# for CNN with batch normalization regularization
##################################
set_seed()
batch_size = 16
model_bnr = Sequential()
model_bnr.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='Same', input_shape=(299, 299, 1)))
model_bnr.add(MaxPooling2D(pool_size=(2, 2)))
model_bnr.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model_bnr.add(BatchNormalization())
model_bnr.add(Activation('relu'))
model_bnr.add(MaxPooling2D(pool_size=(2, 2)))
model_bnr.add(Flatten())
model_bnr.add(Dense(units=128, activation='relu'))
model_bnr.add(Dense(units=num_classes, activation='softmax'))
##################################
# Compiling the network layers
##################################
model_bnr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall()])
##################################
# Displaying the model summary
# for CNN with batch normalization regularization
##################################
print(model_bnr.summary())
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 299, 299, 32) 320
max_pooling2d (MaxPooling2D (None, 149, 149, 32) 0
)
conv2d_1 (Conv2D) (None, 149, 149, 64) 18496
batch_normalization (BatchN (None, 149, 149, 64) 256
ormalization)
activation (Activation) (None, 149, 149, 64) 0
max_pooling2d_1 (MaxPooling (None, 74, 74, 64) 0
2D)
flatten (Flatten) (None, 350464) 0
dense (Dense) (None, 128) 44859520
dense_1 (Dense) (None, 3) 387
=================================================================
Total params: 44,878,979
Trainable params: 44,878,851
Non-trainable params: 128
_________________________________________________________________
None
##################################
# Displaying the model layers
# for CNN with batch normalization regularization
##################################
model_bnr_layer_names = [layer.name for layer in model_bnr.layers]
print("Layer Names:", model_bnr_layer_names)
Layer Names: ['conv2d', 'max_pooling2d', 'conv2d_1', 'batch_normalization', 'activation', 'max_pooling2d_1', 'flatten', 'dense', 'dense_1']
##################################
# Displaying the number of weights
# for each model layer
# for CNN with batch normalization regularization
##################################
for layer in model_bnr.layers:
if hasattr(layer, 'weights'):
print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")
Layer: conv2d, Number of Weights: 2 Layer: max_pooling2d, Number of Weights: 0 Layer: conv2d_1, Number of Weights: 2 Layer: batch_normalization, Number of Weights: 4 Layer: activation, Number of Weights: 0 Layer: max_pooling2d_1, Number of Weights: 0 Layer: flatten, Number of Weights: 0 Layer: dense, Number of Weights: 2 Layer: dense_1, Number of Weights: 2
##################################
# Displaying the number of weights
# for each model layer
# for CNN with batch normalization regularization
##################################
total_parameters = 0
for layer in model_bnr.layers:
layer_parameters = layer.count_params()
total_parameters += layer_parameters
print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)
Layer: conv2d, Parameters: 320 Layer: max_pooling2d, Parameters: 0 Layer: conv2d_1, Parameters: 18496 Layer: batch_normalization, Parameters: 256 Layer: activation, Parameters: 0 Layer: max_pooling2d_1, Parameters: 0 Layer: flatten, Parameters: 0 Layer: dense, Parameters: 44859520 Layer: dense_1, Parameters: 387 Total Parameters in the Model: 44878979
##################################
# Fitting the model
# for CNN with batch normalization regularization
##################################
epochs = 100
set_seed()
model_bnr_history = model_bnr.fit(train_gen,
steps_per_epoch=len(train_gen) // batch_size,
validation_steps=len(test_gen) // batch_size,
validation_data=test_gen, epochs=epochs,
verbose=0)
##################################
# Evaluating the model
# for CNN with batch normalization regularization
# on the independent validation set
##################################
model_bnr_y_pred = model_bnr.predict(test_gen)
45/45 [==============================] - 4s 92ms/step
##################################
# Plotting the loss profile
# for CNN with batch normalization regularization
# on the training and validation sets
##################################
plot_training_history(model_bnr_history, 'CNN With Batch Normalization Regularization : ')
##################################
# Consolidating the predictions
# for CNN with batch normalization regularization
# on the validation set
##################################
model_bnr_predictions = np.array(list(map(lambda x: np.argmax(x), model_bnr_y_pred)))
model_bnr_y_true = test_gen.classes
##################################
# Formulating the confusion matrix
# for CNN with batch normalization regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_bnr_y_true, model_bnr_predictions), columns=classes, index =classes)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250,cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold')
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Batch Normalization Regularization : Validation Set Confusion Matrix',fontsize = 16,weight = 'bold',pad=20);
##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()
##################################
# Calculating the model accuracy
# for CNN with batch normalization regularization
# for the entire validation set
##################################
model_bnr_acc = accuracy_score(model_bnr_y_true, model_bnr_predictions)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for the entire validation set
##################################
model_bnr_results_all = precision_recall_fscore_support(model_bnr_y_true, model_bnr_predictions, average='macro',zero_division = 1)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for each category of the validation set
##################################
model_bnr_results_class = precision_recall_fscore_support(model_bnr_y_true, model_bnr_predictions, average=None, zero_division = 1)
##################################
# Consolidating all model evaluation metrics
# for CNN with batch normalization regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_bnr_all_df = pd.concat([pd.DataFrame(list(model_bnr_results_class)).T,pd.DataFrame(list(model_bnr_results_all)).T])
model_bnr_all_df.columns = metric_columns
model_bnr_all_df.index = ['COVID', 'Normal', 'Viral Pneumonia','Total']
model_bnr_all_df
| Precision | Recall | F-Score | Support | |
|---|---|---|---|---|
| COVID | 0.943231 | 0.900000 | 0.921109 | 240.0 |
| Normal | 0.876448 | 0.945833 | 0.909820 | 240.0 |
| Viral Pneumonia | 0.913793 | 0.883333 | 0.898305 | 240.0 |
| Total | 0.911157 | 0.909722 | 0.909744 | NaN |
##################################
# Consolidating all model evaluation metrics
# for CNN with batch normalization regularization
##################################
model_bnr_model_list = []
model_bnr_measure_list = []
model_bnr_category_list = []
model_bnr_value_list = []
for i in range(3):
for j in range(4):
model_bnr_model_list.append('CNN_BNR')
model_bnr_measure_list.append(metric_columns[i])
model_bnr_category_list.append(model_bnr_all_df.index[j])
model_bnr_value_list.append(model_bnr_all_df.iloc[j,i])
model_bnr_all_summary = pd.DataFrame(zip(model_bnr_model_list,
model_bnr_measure_list,
model_bnr_category_list,
model_bnr_value_list),
columns=['CNN.Model.Name',
'Model.Metric',
'Image.Category',
'Metric.Value'])
##################################
# Formulating the network architecture
# for CNN with dropout and batch normalization regularization
##################################
set_seed()
batch_size = 16
model_dr_bnr = Sequential()
model_dr_bnr.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='Same', input_shape=(299, 299, 1)))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2)))
model_dr_bnr.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model_dr_bnr.add(BatchNormalization())
model_dr_bnr.add(Activation('relu'))
model_dr_bnr.add(Dropout(0.25))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2)))
model_dr_bnr.add(Flatten())
model_dr_bnr.add(Dense(units=128, activation='relu'))
model_dr_bnr.add(Dense(units=num_classes, activation='softmax'))
##################################
# Compiling the network layers
##################################
model_dr_bnr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall()])
##################################
# Displaying the model summary
# for CNN with dropout and
# batch normalization regularization
##################################
print(model_dr_bnr.summary())
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 299, 299, 32) 320
max_pooling2d (MaxPooling2D (None, 149, 149, 32) 0
)
conv2d_1 (Conv2D) (None, 149, 149, 64) 18496
batch_normalization (BatchN (None, 149, 149, 64) 256
ormalization)
activation (Activation) (None, 149, 149, 64) 0
dropout (Dropout) (None, 149, 149, 64) 0
max_pooling2d_1 (MaxPooling (None, 74, 74, 64) 0
2D)
flatten (Flatten) (None, 350464) 0
dense (Dense) (None, 128) 44859520
dense_1 (Dense) (None, 3) 387
=================================================================
Total params: 44,878,979
Trainable params: 44,878,851
Non-trainable params: 128
_________________________________________________________________
None
##################################
# Displaying the model layers
# for CNN with dropout and
# batch normalization regularization
##################################
model_dr_bnr_layer_names = [layer.name for layer in model_dr_bnr.layers]
print("Layer Names:", model_dr_bnr_layer_names)
Layer Names: ['conv2d', 'max_pooling2d', 'conv2d_1', 'batch_normalization', 'activation', 'dropout', 'max_pooling2d_1', 'flatten', 'dense', 'dense_1']
##################################
# Displaying the number of weights
# for CNN with dropout and
# batch normalization regularization
##################################
for layer in model_dr_bnr.layers:
if hasattr(layer, 'weights'):
print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")
Layer: conv2d, Number of Weights: 2 Layer: max_pooling2d, Number of Weights: 0 Layer: conv2d_1, Number of Weights: 2 Layer: batch_normalization, Number of Weights: 4 Layer: activation, Number of Weights: 0 Layer: dropout, Number of Weights: 0 Layer: max_pooling2d_1, Number of Weights: 0 Layer: flatten, Number of Weights: 0 Layer: dense, Number of Weights: 2 Layer: dense_1, Number of Weights: 2
##################################
# Displaying the number of weights
# for CNN with dropout and
# batch normalization regularization
##################################
total_parameters = 0
for layer in model_dr_bnr.layers:
layer_parameters = layer.count_params()
total_parameters += layer_parameters
print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)
Layer: conv2d, Parameters: 320 Layer: max_pooling2d, Parameters: 0 Layer: conv2d_1, Parameters: 18496 Layer: batch_normalization, Parameters: 256 Layer: activation, Parameters: 0 Layer: dropout, Parameters: 0 Layer: max_pooling2d_1, Parameters: 0 Layer: flatten, Parameters: 0 Layer: dense, Parameters: 44859520 Layer: dense_1, Parameters: 387 Total Parameters in the Model: 44878979
##################################
# Fitting the model
# for CNN with dropout and
# batch normalization regularization
##################################
epochs = 100
set_seed()
model_dr_bnr_history = model_dr_bnr.fit(train_gen,
steps_per_epoch=len(train_gen) // batch_size,
validation_steps=len(test_gen) // batch_size,
validation_data=test_gen,
epochs=epochs,
verbose=0)
##################################
# Evaluating the model
# for CNN with dropout and
# batch normalization regularization
# on the independent validation set
##################################
model_dr_bnr_y_pred = model_dr_bnr.predict(test_gen)
45/45 [==============================] - 4s 97ms/step
##################################
# Plotting the loss profile
# for CNN with dropout and
# batch normalization regularization
# on the training and validation sets
##################################
plot_training_history(model_dr_bnr_history, 'CNN With Dropout and Batch Normalization Regularization : ')
##################################
# Consolidating the predictions
# for CNN with dropout and
# batch normalization regularization
# on the validation set
##################################
model_dr_bnr_predictions = np.array(list(map(lambda x: np.argmax(x), model_dr_bnr_y_pred)))
model_dr_bnr_y_true = test_gen.classes
##################################
# Formulating the confusion matrix
# for CNN with dropout and
# batch normalization regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_dr_bnr_y_true, model_dr_bnr_predictions), columns=classes, index =classes)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with dropout and
# batch normalization regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250,cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold')
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Dropout and Batch Normalization Regularization : Validation Set Confusion Matrix',fontsize = 16,weight = 'bold',pad=20);
##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()
##################################
# Calculating the model accuracy
# for CNN with dropout and
# batch normalization regularization
# for the entire validation set
##################################
model_dr_bnr_acc = accuracy_score(model_dr_bnr_y_true, model_dr_bnr_predictions)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with dropout and
# batch normalization regularization
# for the entire validation set
##################################
model_dr_bnr_results_all = precision_recall_fscore_support(model_dr_bnr_y_true, model_dr_bnr_predictions, average='macro',zero_division = 1)
##################################
# Calculating the model
# Precision, Recall, F-score and Support
# for CNN with dropout and
# batch normalization regularization
# for each category of the validation set
##################################
model_dr_bnr_results_class = precision_recall_fscore_support(model_dr_bnr_y_true, model_dr_bnr_predictions, average=None, zero_division = 1)
##################################
# Consolidating all model evaluation metrics
# for CNN with dropout and
# batch normalization regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_dr_bnr_all_df = pd.concat([pd.DataFrame(list(model_dr_bnr_results_class)).T,pd.DataFrame(list(model_dr_bnr_results_all)).T])
model_dr_bnr_all_df.columns = metric_columns
model_dr_bnr_all_df.index = ['COVID', 'Normal', 'Viral Pneumonia','Total']
model_dr_bnr_all_df
| Precision | Recall | F-Score | Support | |
|---|---|---|---|---|
| COVID | 0.950450 | 0.879167 | 0.913420 | 240.0 |
| Normal | 0.779310 | 0.941667 | 0.852830 | 240.0 |
| Viral Pneumonia | 0.923077 | 0.800000 | 0.857143 | 240.0 |
| Total | 0.884279 | 0.873611 | 0.874464 | NaN |
##################################
# Consolidating all model evaluation metrics
# for CNN with dropout and
# batch normalization regularization
##################################
model_dr_bnr_model_list = []
model_dr_bnr_measure_list = []
model_dr_bnr_category_list = []
model_dr_bnr_value_list = []
for i in range(3):
for j in range(4):
model_dr_bnr_model_list.append('CNN_DR_BNR')
model_dr_bnr_measure_list.append(metric_columns[i])
model_dr_bnr_category_list.append(model_dr_bnr_all_df.index[j])
model_dr_bnr_value_list.append(model_dr_bnr_all_df.iloc[j,i])
model_dr_bnr_all_summary = pd.DataFrame(zip(model_dr_bnr_model_list,
model_dr_bnr_measure_list,
model_dr_bnr_category_list,
model_dr_bnr_value_list),
columns=['CNN.Model.Name',
'Model.Metric',
'Image.Category',
'Metric.Value'])
##################################
# Consolidating all the
# CNN model performance measures
##################################
cnn_model_performance_comparison = pd.concat([model_nr_all_summary,
model_dr_all_summary,
model_bnr_all_summary,
model_dr_bnr_all_summary],
ignore_index=True)
##################################
# Consolidating all the precision
# model performance measures
##################################
cnn_model_performance_comparison_precision = cnn_model_performance_comparison[cnn_model_performance_comparison['Model.Metric']=='Precision']
cnn_model_performance_comparison_precision_CNN_NR = cnn_model_performance_comparison_precision[cnn_model_performance_comparison_precision['CNN.Model.Name']=='CNN_NR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_precision_CNN_DR = cnn_model_performance_comparison_precision[cnn_model_performance_comparison_precision['CNN.Model.Name']=='CNN_DR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_precision_CNN_BNR = cnn_model_performance_comparison_precision[cnn_model_performance_comparison_precision['CNN.Model.Name']=='CNN_BNR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_precision_CNN_DR_BNR = cnn_model_performance_comparison_precision[cnn_model_performance_comparison_precision['CNN.Model.Name']=='CNN_DR_BNR'].loc[:,"Metric.Value"]
##################################
# Combining all the precision
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_precision_plot = pd.DataFrame({'CNN_NR': cnn_model_performance_comparison_precision_CNN_NR.values,
'CNN_DR': cnn_model_performance_comparison_precision_CNN_DR.values,
'CNN_BNR': cnn_model_performance_comparison_precision_CNN_BNR.values,
'CNN_DR_BNR': cnn_model_performance_comparison_precision_CNN_DR_BNR.values},
index=cnn_model_performance_comparison_precision['Image.Category'].unique())
cnn_model_performance_comparison_precision_plot
| CNN_NR | CNN_DR | CNN_BNR | CNN_DR_BNR | |
|---|---|---|---|---|
| COVID | 0.890295 | 0.906504 | 0.943231 | 0.950450 |
| Normal | 0.671378 | 0.904255 | 0.876448 | 0.779310 |
| Viral Pneumonia | 0.810000 | 0.758741 | 0.913793 | 0.923077 |
| Total | 0.790558 | 0.856500 | 0.911157 | 0.884279 |
##################################
# Plotting all the precision
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_precision_plot = cnn_model_performance_comparison_precision_plot.plot.barh(figsize=(10, 6), width=0.90)
cnn_model_performance_comparison_precision_plot.set_xlim(0.00,1.00)
cnn_model_performance_comparison_precision_plot.set_title("Model Comparison by Precision Performance on Validation Data")
cnn_model_performance_comparison_precision_plot.set_xlabel("Precision Performance")
cnn_model_performance_comparison_precision_plot.set_ylabel("Image Categories")
cnn_model_performance_comparison_precision_plot.grid(False)
cnn_model_performance_comparison_precision_plot.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
for container in cnn_model_performance_comparison_precision_plot.containers:
cnn_model_performance_comparison_precision_plot.bar_label(container, fmt='%.5f', padding=-50, color='white', fontweight='bold')
##################################
# Consolidating all the recall
# model performance measures
##################################
cnn_model_performance_comparison_recall = cnn_model_performance_comparison[cnn_model_performance_comparison['Model.Metric']=='Recall']
cnn_model_performance_comparison_recall_CNN_NR = cnn_model_performance_comparison_recall[cnn_model_performance_comparison_recall['CNN.Model.Name']=='CNN_NR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_recall_CNN_DR = cnn_model_performance_comparison_recall[cnn_model_performance_comparison_recall['CNN.Model.Name']=='CNN_DR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_recall_CNN_BNR = cnn_model_performance_comparison_recall[cnn_model_performance_comparison_recall['CNN.Model.Name']=='CNN_BNR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_recall_CNN_DR_BNR = cnn_model_performance_comparison_recall[cnn_model_performance_comparison_recall['CNN.Model.Name']=='CNN_DR_BNR'].loc[:,"Metric.Value"]
##################################
# Combining all the recall
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_recall_plot = pd.DataFrame({'CNN_NR': cnn_model_performance_comparison_recall_CNN_NR.values,
'CNN_DR': cnn_model_performance_comparison_recall_CNN_DR.values,
'CNN_BNR': cnn_model_performance_comparison_recall_CNN_BNR.values,
'CNN_DR_BNR': cnn_model_performance_comparison_recall_CNN_DR_BNR.values},
index=cnn_model_performance_comparison_recall['Image.Category'].unique())
cnn_model_performance_comparison_recall_plot
| CNN_NR | CNN_DR | CNN_BNR | CNN_DR_BNR | |
|---|---|---|---|---|
| COVID | 0.879167 | 0.929167 | 0.900000 | 0.879167 |
| Normal | 0.791667 | 0.708333 | 0.945833 | 0.941667 |
| Viral Pneumonia | 0.675000 | 0.904167 | 0.883333 | 0.800000 |
| Total | 0.781944 | 0.847222 | 0.909722 | 0.873611 |
##################################
# Plotting all the recall
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_recall_plot = cnn_model_performance_comparison_recall_plot.plot.barh(figsize=(10, 6), width=0.90)
cnn_model_performance_comparison_recall_plot.set_xlim(0.00,1.00)
cnn_model_performance_comparison_recall_plot.set_title("Model Comparison by Recall Performance on Validation Data")
cnn_model_performance_comparison_recall_plot.set_xlabel("Recall Performance")
cnn_model_performance_comparison_recall_plot.set_ylabel("Image Categories")
cnn_model_performance_comparison_recall_plot.grid(False)
cnn_model_performance_comparison_recall_plot.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
for container in cnn_model_performance_comparison_recall_plot.containers:
cnn_model_performance_comparison_recall_plot.bar_label(container, fmt='%.5f', padding=-50, color='white', fontweight='bold')
##################################
# Consolidating all the f-score
# model performance measures
##################################
cnn_model_performance_comparison_fscore = cnn_model_performance_comparison[cnn_model_performance_comparison['Model.Metric']=='F-Score']
cnn_model_performance_comparison_fscore_CNN_NR = cnn_model_performance_comparison_fscore[cnn_model_performance_comparison_fscore['CNN.Model.Name']=='CNN_NR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_fscore_CNN_DR = cnn_model_performance_comparison_fscore[cnn_model_performance_comparison_fscore['CNN.Model.Name']=='CNN_DR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_fscore_CNN_BNR = cnn_model_performance_comparison_fscore[cnn_model_performance_comparison_fscore['CNN.Model.Name']=='CNN_BNR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_fscore_CNN_DR_BNR = cnn_model_performance_comparison_fscore[cnn_model_performance_comparison_fscore['CNN.Model.Name']=='CNN_DR_BNR'].loc[:,"Metric.Value"]
##################################
# Combining all the f-score
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_fscore_plot = pd.DataFrame({'CNN_NR': cnn_model_performance_comparison_fscore_CNN_NR.values,
'CNN_DR': cnn_model_performance_comparison_fscore_CNN_DR.values,
'CNN_BNR': cnn_model_performance_comparison_fscore_CNN_BNR.values,
'CNN_DR_BNR': cnn_model_performance_comparison_fscore_CNN_DR_BNR.values},
index=cnn_model_performance_comparison_fscore['Image.Category'].unique())
cnn_model_performance_comparison_fscore_plot
| CNN_NR | CNN_DR | CNN_BNR | CNN_DR_BNR | |
|---|---|---|---|---|
| COVID | 0.884696 | 0.917695 | 0.921109 | 0.913420 |
| Normal | 0.726577 | 0.794393 | 0.909820 | 0.852830 |
| Viral Pneumonia | 0.736364 | 0.825095 | 0.898305 | 0.857143 |
| Total | 0.782546 | 0.845728 | 0.909744 | 0.874464 |
##################################
# Plotting all the fscore
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_fscore_plot = cnn_model_performance_comparison_fscore_plot.plot.barh(figsize=(10, 6), width=0.90)
cnn_model_performance_comparison_fscore_plot.set_xlim(0.00,1.00)
cnn_model_performance_comparison_fscore_plot.set_title("Model Comparison by F-Score Performance on Validation Data")
cnn_model_performance_comparison_fscore_plot.set_xlabel("F-Score Performance")
cnn_model_performance_comparison_fscore_plot.set_ylabel("Image Categories")
cnn_model_performance_comparison_fscore_plot.grid(False)
cnn_model_performance_comparison_fscore_plot.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
for container in cnn_model_performance_comparison_fscore_plot.containers:
cnn_model_performance_comparison_fscore_plot.bar_label(container, fmt='%.5f', padding=-50, color='white', fontweight='bold')
from IPython.display import display, HTML
display(HTML("<style>.rendered_html { font-size: 15px; font-family: 'Trebuchet MS'; }</style>"))